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Ultrasound images are affected by the well known speckle phenomenon, that degrades their perceived quality. In recent years, several denoising approaches have been proposed. Among all, those belonging to the non-local (NL) family have shown interesting performance. The main difference among the proposed NL filters is the metric adopted for measuring the similarity between patches. Within this manuscript, a statistical metric based on the ratio between two patches is presented. Compared to other statistical measurements, the proposed one is able to take into account the texture of the patch, to consider a weighting kernel and to limit the computational burden. A comparative analysis with other despeckling filters is presented. The method provided good balance between noise reduction and details preserving both in case of simulated (by means of Field II software) and real (breast tumor) datasets.

The topic of denoising magnetic resonance (MR) images is considered in this paper. More in detail, an enhanced Non-Local Means (NLM) filter using the Kolmogorov-Smirnov (KS) distance is proposed. The KS-NLM approach estimates the similarity between image patches by computing the KS distance. To overcome that NLM filters assign the same role to all pixels in patches, that is, not privileging the central one, we propose a new filter, namely the Anisotropic Weighted KS-NLM (Aw KS-NLM), which better deals with central pixels within the patches by, on one hand, including a suitable weighted strategy and, on the other, by performing a local anisotropy analysis. The Aw KS-NLM has been compared to other existing non-local Means (NLM) methodologies in both MRI simulated and real datasets. The results provide excellent noise reduction and image-detail preservation.

Large-scale brain activity evolves dynamically over time across multiple time-scales. The structural connectome imposes a spatial network constraint since two structurally connected brain regions are more likely to coordinate their activity. It also imposes a temporal network constraint by virtue of time delays via signal transmission, which has modulatory effects on oscillatory signals. Specifically, the lengths of the structural bundles, their widths, myelination, and the topology of the structural connectome influence the timing of the interactions across the brain network. Together, they define a space-time structure (topochronic map) spanned by connection strengths (space) and signal transmission delays (time), which together establish the deterministic scaffold underlying the evolution of brain dynamics. We introduce a novel in vivo approach for directly measuring functional delays across the whole brain using magneto/electroencephalography and integrating them with the structural connectome derived from magnetic resonance imaging. We developed a map of the functional delays characterizing the connections across the human brain and a map of the corresponding functional velocities. This yields a topochronic map of the human brain dynamics. The functional delays are tightly regulated, with a trend showing, as expected, that larger structural bundles had faster velocities, with the delays varying much less than expected if they only depended upon distance. Then, we estimated the delays from magnetoencephalography (MEG) data in a cohort of multiple sclerosis patients, who have damaged myelin sheaths, and demonstrated that patients showed greater delays across the whole network than a matched control group. Furthermore, within each patient, individual lesioned connections were slowed down more than unaffected ones. Our technique provides a practical approach for estimating functional transmission delays in vivo at the single-subject and single-fiber level and, thus, opens the possibility for novel diagnostic and curative interventions as well as providing empirical, subject-specific constraints to tailor brain models.

In the framework of inverse scattering problems, this paper investigates the use of different artificial-neural network architectures for imaging purposes by processing the data collected at receivers locations in a multiview-multistatic fashion. Generally, this type of problems is strongly nonlinear and ill-posed, thus the development of fast and reliable approaches is paramount for practical implementations. In the last years, machine learning approaches have proved to be very promising to recover quantitatively the electromagnetic features of objects located in an investigation domain, but at the expense of large data sets required for the training procedure. More in detail, this communication tries to explore the role of the network topology by exploiting three different scenarios with an increasingly higher degree of non-linearity.

In this paper, we present the magnetoencephalography system developed by the Institute of Applied Sciences and Intelligent Systems of the National Research Council and recently installed in a clinical environment. The system employ ultra high sensitive magnetic sensors based on superconducting quantum interference devices (SQUIDs). SQUID sensors have been realized using a standard trilayer technology that ensures good performances over time and a good signal-to-noise ratio, even at low frequencies. They exhibit a spectral density of magnetic field noise as low as 2 fT/Hz 1/2 . Our system consists of 163 fully-integrated SQUID magnetometers, 154 channels and 9 references, and all of the operations are performed inside a magnetically-shielded room having a shielding factor of 56 dB at 1 Hz. Preliminary measurement have demonstrated the effectiveness of the MEG system to perform useful measurements for clinical and neuroscience investigations. Such a magnetoencephalography is the first system working in a clinical environment in Italy.

The current paper proposes a method to estimate phase to phase cross-frequency coupling between brain areas, applied to broadband signals, without any a priori hypothesis about the frequency of the synchronized components. N:m synchronization is the only form of cross-frequency synchronization that allows the exchange of information at the time resolution of the faster signal, hence likely to play a fundamental role in large-scale coordination of brain activity. The proposed method, named cross-frequency phase linearity measurement (CF-PLM), builds and expands upon the phase linearity measurement, an iso-frequency connectivity metrics previously published by our group. The main idea lies in using the shape of the interferometric spectrum of the two analyzed signals in order to estimate the strength of cross-frequency coupling. Here, we demonstrate that the CF-PLM successfully retrieves the (different) frequencies of the original broad-band signals involved in the connectivity process. Furthermore, if the broadband signal has some frequency components that are synchronized in iso-frequency and some others that are synchronized in cross-frequency, our methodology can successfully disentangle them and describe the behaviour of each frequency component separately. We first provide a theoretical explanation of the metrics. Then, we test the proposed metric on simulated data from coupled oscillators synchronized in iso- and cross-frequency (using both Rössler and Kuramoto oscillator models), and subsequently apply it on real data from brain activity, using source-reconstructed Magnetoencephalography (MEG) data. In the synthetic data, our results show reliable estimates even in the presence of noise and limited sample sizes. In the real signals, components synchronized in cross-frequency are retrieved, together with their oscillation frequencies. All in all, our method is useful to estimate n:m synchronization, based solely on the phase of the signals (independently of the amplitude), and no a-priori hypothesis is available about the expected frequencies. Our method can be exploited to more accurately describe patterns of cross-frequency synchronization and determine the central frequencies involved in the coupling.

Stimulus perception is assumed to involve the (fast) detection of sensory inputs and their (slower) integration. The capacity of the brain to quickly adapt, at all times, to unexpected stimuli suggests that the interplay between the slow and fast processes happens at short timescales. We hypothesised that, even during resting-state, the flow of information across the brain regions should evolve quickly, but not homogeneously in time. Applying edge-wise connectivity to high temporal-resolution Magnetoencephalography (MEG) signals, we estimate the persistence of the information in functional interactions across the brain. The characterization of brain network edges according to their information storage capability naturally partitions the brain into two anatomically distinct networks. The short storage network (SSN) is localized in the regions that typically belong to the dorsal and ventral streams during perceptive tasks, while the long storage network (LSN) hinges on classically associative areas. Finally, we show that only a subset of the brain regions, which we name the multi-storage core (MSC), belong to both networks. The MSC is hypothesized to play a role in the communication between the (otherwise) segregated subnetworks.

In this communication, a low-cost radar-sensor-based apparatus for contactless hand gesture recognition via Doppler signature analysis is proposed. The raw reflected signal, after some pre-processing, is analysed via its time-frequency representation, known as spectrogram. This information is then exploited to train a convolutional neural network (CNN) to perform the classification step. The whole procedure was tested on an in-house experimental data set composed of four different hand gestures, showing good performance and reaching an accuracy of approximately 97%. Finally, the classification performance was tested also in a cluttered environment which includes the presence of a strong echo close to the target.

This paper proposes a fully-connected artificial neural network (ANN) approach for addressing the full-wave inverse scattering problem in a quantitative fashion. The proposed scheme processes the scattered field samples collected at receivers locations and provides as output an estimate of the unknown complex permittivity in strongly non-linear scenarios. The proposed approach requires a proper training step, which is also addressed via an automatic randomly-shaped complex profile generator inspired by the statistical distribution of breast biological tissues, and is almost real-time in the recovery step. Several representative numerical tests were carried out to evaluate the performance of the proposed method and to validate the use of ANN for quantitative imaging purposes in biological-inspired scenarios.

Background Brain areas need to coordinate their activity in order to enable complex behavioral responses. Synchronization is one of the mechanisms neural ensembles use to communicate. While synchronization between signals operating at similar frequencies is fairly straightforward, the estimation of synchronization occurring between different frequencies of oscillations has proven harder to capture. One specifically hard challenge is to estimate cross-frequency synchronization between broadband signals when no a priori hypothesis is available about the frequencies involved in the synchronization. Methods In the present manuscript, we expand upon the phase linearity measurement, an iso-frequency synchronization metrics previously developed by our group, in order to provide a conceptually similar approach able to detect the presence of cross-frequency synchronization between any components of the analyzed broadband signals. Results The methodology has been tested on both synthetic and real data. We first exploited Gaussian process realizations in order to explore the properties of our new metrics in a synthetic case study. Subsequently, we analyze real source-reconstructed data acquired by a magnetoencephalographic system from healthy controls in a clinical setting to study the performance of our metrics in a realistic environment. Conclusions In the present paper we provide an evolution of the PLM methodology able to reveal the presence of cross-frequency synchronization between broadband data.

The problem of cleaning magnetoencephalographic data is addressed in this manuscript. At present, several denoising procedures have been proposed in the literature, nevertheless their adoption is limited due to the difficulty in implementing and properly tuning the algorithms. Therefore, as of today, the gold standard remains manual cleaning. We propose an approach developed with the aim of automating each step of the manual cleaning. Its peculiarities are the ease of implementation and using and the remarkable reproducibility of the results. Interestingly, the algorithm has been designed to imitate the reasoning behind the manual procedure, carried out by trained experts. Our statistical analysis shows that no significant differences can be found between the two approaches.

The monitoring of vital signs represents an important measure in several fields which also go beyond the clinical environment. Nowadays, different technologies are employed to fulfil this aim, and among them, remote monitoring devices are becoming more and more popular. Following this trend, here we present an in-house, experimental ultrasound system for heartbeat detection which does not require any contact with the subject and allows remote monitoring of heart rate and its variability. The system works by transmitting ultrasound waves and receiving the echoes after the reflection on subject’s pit of the neck. By comparing the phases of transmitted and received waves, it measures distances with high accuracy, and thus it can detect the sub-millimetre movements of the skin due to the pressure waves produced by heart contractions. Conversely from other existing ultrasound systems, the novelty proposed in this paper is in the multi-channel strategy to address the null point issue and selecting the best one per each measure, and in the use of an adaptive heartbeat detection algorithm. Due to the adopted transducers and electronics, the proposed system is characterised by a low cost of production. Performance analyses were carried out both in case of phantoms and volunteers to test the system. The results look promising as it was able to detect the heartbeats with reliability close to standard electrocardiography, achieving good performance also in measuring the heart rate variability. An average mean absolute error (MAE) of 0.02 beats per minute (bpm) is assessed on real data.

Speckle noise greatly degrades the quality of ultrasound images. Being signal dependent, it requires the design of specific filters in order to be reduce. Within this manuscript, $a$ novel approach for despeckling ultrasound images is proposed. The methodology belongs to the Non Local Means family. The novelty consists in the methodology adopted for measuring patches similarity. In brief, the statistical distribution of the ratio image patch is estimated and compared to the theoretical Cumulative Distribution Function. More in detail, the Kolmogorov-Smirnov distance is adopted for measuring the similarity between the two distribution. The method, namely KSR-NLM, has shown to achieve good denoising performances both in case of synthetic and real datasets.

The problem of describing how different brain areas interact between each other has been granted a great deal of attention in the last years. The idea that neuronal ensembles behave as oscillators and that they communicate through synchronization is now widely accepted. To this regard, EEG and MEG provide the signals that allow the estimation of such communication in vivo. Hence, phase-based metrics are essential. However, the application of phased-based metrics for measuring brain connectivity has proved problematic so far, since they appear to be less resilient to noise as compared to amplitude-based ones. In this paper, we address the problem of designing a purely phase-based brain connectivity metric, insensitive to volume conduction and resilient to noise. The proposed metric, named phase linearity measurement (PLM), is based on the analysis of similar behaviors in the phases of the recorded signals. The PLM is tested in two simulated datasets as well as in real MEG data acquired at the Naples MEG center. Due to its intrinsic characteristics, the PLM shows considerable noise rejection properties as compared to other widely adopted connectivity metrics. We conclude that the PLM might be valuable in order to allow better estimation of phase-based brain connectivity.

A novel metric for estimating connectivity between brain areas, namely the Phase Linearity Measurement (PLM), is presented. The purpose consists in measuring the amount of information exchanged between brain areas. Such scope is achieved by analyzing the similarities between the recorded signal phases. The PLM has been designed for exploiting both Electroencephalographic (EEG) and Magnetoencephalographic (MEG) data. We compared the results achieved by PLM in case of real MEG data with a widely adopted phase based connectivity metric, the Phase Lag Index (PLI). The PLM is characterized by interesting results, mainly in terms of noise resiliency.

Data coming from any acquisition system, such as Magnetic Resonance Imaging ones, are affected by noise. Although modern high field scanners can reach high Signal to Noise Ratios, in some circumstances, for example in case of very weak signals due to a specific acquisition sequence, noise becomes a critical issue that has to be properly handled. In the last years methods based on the so called Non Local Mean have proven to be very effective in denoising tasks. The idea of these filters is to find similar patches across the image and to jointly exploit them to obtain the restored image. A critical point is the distance metric adopted for measuring similarity. Within this manuscript, we propose a filtering technique based on the Kolmogorov-Smirnov distance. The main innovative aspect of the proposed method consists of the criteria adopted for finding similar pixels across the image: it is based on the statistics of the points rather than the widely adopted weighted Euclidean distance. More in details, the Cumulative Distribution Functions of different pixels are evaluated and compared in order to measure their similarities, exploiting a stack of images of the same slice acquired with different acquisition parameters. To quantitatively and qualitatively assess the performances of the approach, a comparison with other widely adopted denoising filters in case of both simulated and real datasets has been carried out. The obtained results confirm the validity of the proposed solution.

International audience; In recent years, the meaning of successful living has moved from extending lifetime to improving the quality of aging, mainly in terms of high cognitive and physical functioning together with avoiding diseases. In healthy elderly, falls represent an alarming accident both in terms of number of events and the consequent decrease in the quality of life. Stability control is a key approach for studying the genesis of falls, for detecting the event and trying to develop methodologies to prevent it. Wearable sensors have proved to be very useful in monitoring and analyzing the stability of subjects. Within this manuscript, a review of the approaches proposed in the literature for fall risk assessment, fall prevention and fall detection in healthy elderly is provided. The review has been carried out by using the most adopted publication databases and by defining a search strategy based on keywords and boolean algebra constructs. The analysis aims at evaluating the state of the art of such kind of monitoring, both in terms of most adopted sensor technologies and of their location on the human body. The review has been extended to both dynamic and static analyses. In order to provide a useful tool for researchers involved in this field, the manuscript also focuses on the tests conducted in the analyzed studies, mainly in terms of characteristics of the population involved and of the tasks used. Finally, the main trends related to sensor typology, sensor location and tasks have been identified.

The analysis of brain connectivity is gaining interest in recent years due to the relevant information it carries about the functioning of the brain in health and in disease. In brief, it consists in measuring the statistical dependencies between signals generated by different brain regions. Several metrics have been proposed in literature, related to three families: amplitude based, phase based on jointly amplitude and phase based. Due to the large amount of noise that typically affects the estimation of the connectivity maps, averaging over several epochs of a population is normally carried out. We propose a novel phase based metric, namely the Phase Linearity Metric (PLM), that is resilient to noise and volume conduction, bearing promise to lower the number of epochs needed for a reliable measurement. The comparison with the widely adopted PLI connectivity metric confirms the effectiveness of the PLM.

Automatic Target Recognition (ATR) aims at detecting the presence and at recognizing the typology and the orientation of targets within a scenario, by using an unsupervised approach. In Syntethic Aperture Radar imaging this turns to be a difficult task due to the specific characteristics of clutter and background noise. Within this manuscript a new two-steps ATR algorithm based on Kolmogorov-Smirnov test is presented. The method has been tested on real MSTAR datasets showing interesting performances.

The random noise arising from the acquisition process of magnetic resonance images negatively influences the diagnostic process. Thus, a denoising step is commonly adopted in case of many image processing and analysis tasks. The main criticism of denoising filters is to preserve edges and details while reducing noise across the image. Within this manuscript the a novel denoising filter, namely the KS-NLM, is proposed, with the aim of improving the denoising quality. The approach belongs to the Non Local Means (NLM) family as it exploits groups of similar pixels. The novelty consists in the similarity measurement, that is based on the statistical distribution of data instead of the similarity between the local textures of the image. In particular, the Cumulative Distribution Function for each pixel is evaluated from the acquired data, and subsequently the Kolmogorov-Smirnov distance is computed as similarity metric. Similar pixels are subsequently fused. The method has been tested on a real dataset acquired via a 3T scanner, and its performances have been compared to other state-of-art filters.

Ultrasound images are characterized by speckle, a multiplicative noise that degrades their quality. In the last decades, several efforts have been done for developing effective denoising filters able to provide effective signal regularization and noise preservation. Recently, the so-called Non Local Mean approaches have proven to be well suited for such kind of problems. Within this manuscript, a new despeckling filter for ultrasound data is presented, developed in the Non Local Mean framework that jointly exploits several acquired video frames for reducing speckle. The main novelty consists in the metric adopted for the evaluation of patches similarity, which is based on the statistical properties of the acquired data. More in detail, the Kolmogorov-Smirnov distance between the cumulative distribution functions of the involved pixels, computed on the available frames, is evaluated. The method has been tested on simulated data and compared to other state of art despeckling filters belonging to different families, showing interesting performances in combining good details preservation with effective noise reduction.

Background Within this manuscript a noise filtering technique for magnetic resonance image stack is presented. Magnetic resonance images are usually affected by artifacts and noise due to several reasons. Several denoising approaches have been proposed in literature, with different trade-off between computational complexity, regularization and noise reduction. Most of them is supervised, i.e. requires the set up of several parameters. A completely unsupervised approach could have a positive impact on the community. Results The method exploits Markov random fields in order to implement a 3D maximum a posteriori estimator of the image. Due to the local nature of the considered model, the algorithm is able do adapt the smoothing intensity to the local characteristics of the images by analyzing the 3D neighborhood of each voxel. The effect is a combination of details preservation and noise reduction. The algorithm has been compared to other widely adopted denoising methodologies in MRI. Both simulated and real datasets have been considered for validation. Real datasets have been acquired at 1.5 and 3 T. The methodology is able to provide interesting results both in terms of noise reduction and edge preservation without any supervision. Conclusions A novel method for regularizing 3D MR image stacks is presented. The approach exploits Markov random fields for locally adapt filter intensity. Compared to other widely adopted noise filters, the method has provided interesting results without requiring the tuning of any parameter by the user.

Background and Objective Speckle phenomenon strongly affects UltraSound (US) images. In the last years, several efforts have been done in order to provide an effective denoising methodology. Although good results have been achieved in terms of noise reduction effectiveness, most of the proposed approaches are not characterized by low computational burden and require the supervision of an external operator for tuning the input parameters. Methods Within this manuscript, a novel approach is investigated, based on Wiener filter. Working in the frequency domain, it is characterized by high computational efficiency. With respect to classical Wiener filter, the proposed Enhanced Wiener filter is able to locally adapt itself by tuning its kernel in order to combine edges and details preservation with effective noise reduction. This characteristic is achieved by implementing a Local Gaussian Markov Random Field for modeling the image. Due to its intrinsic characteristics, the computational burden of the algorithm is sensibly low compared to other widely adopted filters and the parameter tuning effort is minimal, being well suited for quasi real time applications. Results The approach has been tested on both simulated and real datasets, showing interesting performances compared to other state of art methods. Conclusions A novel denoising method for UltraSound images is proposed. The approach is able to combine low computational burden with interesting denoising performances and details preservation.

Ultrasound images are affected by the speckle phenomenon, a multiplicative noise that degrades image quality. Several methods for denoising have been proposed in recent years, based on different approaches. The so-called non-local mean is considered the state-of-the-art method; the idea is to find similar patches across the image and exploit them to regularize the image. The method proposed here is in the non-local family, although instead of partitioning the target image in patches, it works pixelwise. The similarity between pixels is evaluated by analyzing their statistical behavior, in particular, by measuring the Kolmogorov–Smirnov distance between their distributions. To make this possible, a stack of acquired images is required. The proposed method has been tested on both simulated and real data sets and compared with other widely adopted techniques. Performance is interesting, with quality parameters and visual inspection confirming such findings.

A novel application of l1-norm minimization in the MRI field is presented. The novel methodology, called Intra Voxel Analysis (IVA), by combining different acquisitions with standard resolution, is able to investigate the presence of different contributions, i.e., of different tissues, inside each imaged voxel. The approach is somehow similar to spectroscopy, but instead of searching different resonance frequencies, it discriminates within each voxel the tissues characterized by different spin-spin relaxation times. The proposed methodology is able to work on MR images acquired at full resolution by using any acquisition scheme. A phantom has been built and images for testing the approach.

One of the main challenges in Interferometric Synthetic Aperture Radar (SAR) is the accurate height reconstruction of the observed scene. Recently, approaches based on Extended Kalman Filter (EKF) have been proposed. Most of them are based on the hypothesis of height profile continuity. Such condition greatly reduces their applicability, being only valid for particular scenarios. Within this paper, we present a novel Kalman-based height reconstruction approach, specifically designed to work with multichannel data related to any type of scenario, both smooth or sharp. The novelty of the technique consists in its ability in detecting and correctly handling sharp height discontinuities while regularizing smooth areas. The approach is able to maintain the high computational efficiency typical of EKF and to work in an almost unsupervised way. The methodology has been tested and validated on both simulated and real X-band (TerraSAR-X and COSMO-SkyMed) high-resolution data sets. Reported results are encouraging and interesting, showing the correctness and the validity of the proposed approach.

Speckle phenomenon strongly affects Ultra-Sound (US) images. Over the past last years, several efforts have been done in order to provide an effective denoising methodology. Although good results have been achieved in terms of noise reduction effectiveness, most of the proposed approaches are characterized by a high computational load and require the supervision of an external operator for tuning the input parameters. Within this manuscript, a novel approach for noise reduction is investigated, based on Wiener filter. With respect to classical Wiener filter, the proposed Enhanced Wiener filter is able to locally adapt itself. By automatically tuning its kernel a good combination of edges and details preservation with effective noise reduction can be reached. This behavior is achieved by implementing a Local Gaussian Markov Random Field for modeling the image. Due to its intrinsic characteristics, the computational burden of the algorithm is sensibly low compared to other widely adopted filters and the parameter tuning effort is minimal. Results on a simulated dataset are reported, showing the interesting performances of the approach.

Purpose The article provides a practical method for generating T1 and T2 maps from multiple, sequentially acquired Spin Echo images. First results on real data for diagnostic purposes are presented and discussed in order to investigate the possible clinical usages of the proposed technique. The main advantage of the method is its ability in identifying even very small variation in one or both relaxation time maps, allowing a more clear differentiation of tissues across the whole image, instead of highlighting only high vascularized regions, such as in contrast medium based images. Theory and Methods For the joint estimation of relaxation time maps, the method exploits the full complex acquired data instead of amplitude acquisitions. This allows having an estimator able to provide unbiased results even in case of tissues characterized by weak Signal to Noise Ratio. Results The effectiveness of the proposed approach is shown on different real case studies: a phantom dataset for quantitative analysis and three human brain datasets for diagnostic purposes. Conclusion The proposed method shows very interesting and promising results when applied to clinical datasets. The innovative technique is able to evaluate the biological topography and highlight lesions not otherwise easily distinguishable with standard examinations. © 2015 Wiley Periodicals, Inc. Concepts Magn Reson Part A 43A: 166–176, 2015.

In this letter, an innovative technique for change detection in urban areas using very high resolution synthetic aperture radar multichannel stacks is proposed. Instead of using the amplitude image, as in classical change detection approaches, the proposed technique uses the full complex image in a Markovian framework. The complex data are modeled using Markov random field hyperparameters, which are particular local parameters that take into account the spatial correlation between pixels. Starting from two data sets, the pre- and the postevent ones, the proposed algorithm, first, estimates the two hyperparameter maps and, then, compares the similarity between them. If a change occurs between the pre- and the postevent acquisitions, the statistical distribution of the hyperparameter maps will change. The maximum distance between the two obtained statistical distributions provides an index of changes. This sort of spatial correlation maps is computed using statistical estimation techniques, while the similarity comparison is computed using the two-step Kolmogorov-Smirnov statistic test. The algorithm is validated on simulated data and tested on real COSMO-SkyMed data acquired on the area of Naples, showing interesting and promising results.

Historically, the extraction of coastal line has been performed exploiting optical images, but in the last two decades, some approaches working with synthetic aperture radar (SAR) data have been proposed. Recently, these approaches have been gaining interest due to the availability of high-resolution SAR images. In this letter, a technique for coastal line retrieval from multichannel SAR images is presented. The detection problem is faced in the statistical estimation framework, in particular, exploiting Bayesian estimation theory. The proposed technique is able to detect sea boundaries at full resolution and low error rate in a totally unsupervised way. The performance of the method has been tested using high-resolution COSMO-SkyMed data sets acquired on the Bay of Naples, showing the high accuracy of the proposed technique.

A technique for analyzing the composition of each voxel, in the magnetic resonance imaging (MRI) framework, is presented. By combining different acquisitions, a novel methodology, called intra voxel analysis (IVA), for the detection of multiple tissues and the estimation of their spin-spin relaxation times is proposed. The methodology exploits the sparse Bayesian learning (SBL) approach in order to solve a highly underdetermined problem imposing the solution sparsity. IVA, developed for spin echo imaging sequence, can be easily extended to any acquisition scheme. For validating the approach, simulated and real data sets are considered. Monte Carlo simulations have been implemented for evaluating the performances of IVA compared to methods existing in literature. Two clinical datasets acquired with a 3T scanner have been considered for validating the approach. With respect to other approaches presented in literature, IVA has proved to be more effective in the voxel composition analysis, in particular in the case of few acquired images. Results are interesting and very promising: IVA is expected to have a remarkable impact on the research community and on the diagnostic field.

A novel approach for speckle reduction in SAR images is presented. An enhanced version of the Wiener Filter is proposed in order to locally adapt the filter characteristic to the image behavior, modelled by Markov Random Fields. First results on simulated and real data are reported.

The aim of building edge detection is to obtain a map of man-made structure edges of the investigated scene. Different detectors have been developed exploiting synthetic aperture radar (SAR) data, based on the use of the reflectivity difference (working with SAR amplitude images) or of the phase difference (working with SAR interferometric images) between neighboring pixels. In this letter, a novel approach using jointly both the amplitudes and the interferometric phase of two complex SAR images is proposed, based on the hypothesis that information related to building edges can be retrieved in the two data domains. The technique is based on stochastic estimation theory, exploiting, in particular, Markov random fields. Compared to classical amplitude-based edge detectors and to phase-based ones, the proposed method shows an improvement in terms of detection accuracy, false alarm rate, and building shape recovery. The algorithm has been tested and analyzed using simulated data and validated on L-band and X-band real data sets.

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Field inhomogeneities in Magnetic Resonance Imaging (MRI) can cause blur or image distortion as they produce off-resonance frequency at each voxel. These effects can be corrected if an accurate field map is available. Field maps can be estimated starting from the phase of multiple complex MRI data sets. In this paper we present a technique based on statistical estimation in order to reconstruct a field map exploiting two or more scans. The proposed approach implements a Bayesian estimator in conjunction with the Graph Cuts optimization method. The effectiveness of the method has been proven on simulated and real data.

In this letter, a statistical-based approach to recover layover solution in synthetic aperture radar (SAR) images is proposed. The aim of this letter is to develop a methodology in order to separate different scattering contributions collapsed in a single SAR image pixel. After a brief discussion about layover, the proposed model is presented, followed by a discussion about achievable performances using Cramer-Rao lower bounds. In the final part of this letter, the performances of a maximum likelihood estimator are evaluated in a simulated data scenario, showing the effectiveness of the method.

In this manuscript, a technique for speckle noise reduction in ultrasound images is presented. The method exploits Wiener filter and is able to take into account spatial correlation among noise samples. With respect to classical Wiener filter approach developed in independence hypothesis, the methodology is able to sensibly improve filtering performances, at the cost of no computational time increase. Results on realistic simulated datasets are reported, showing the effectiveness of the approach.

Within this manuscript a novel Multi-channel InSAR phase unwrapping method is proposed. The approach implements an Extended Kalman Filter for jointly unwrap the phase and regularize the result. The novelty of the methodology consists in the probabilistic data association step that has been implemented in order to improve the robustness of EKF for PhU. Encouraging results on simulated dataset are reported.

Relaxation time estimation in MRI field can be helpful in clinical diagnosis. In particular, T1 and T2 changes can be related to tissues modification, being an effective tool for detecting the presence of several pathologies and measure their development, thus their estimation is a useful research field. Currently, most techniques work pixel-wise, and transfer the noise reduction task to post processing filters. A novel method for estimating spin-spin and spin-lattice relaxation times is proposed. The approach exploits Markov Random Field theory for modeling the unknown data and implements an a posteriori estimator in the Bayesian framework. The effect is the joint parameters estimation and noise reduction. Proposed methodology, with respect to already existing techniques, is able to provide effective results while preserving details also in case of few acquisitions or severe signal to noise ratio. The algorithm has been tested on both simulated and real datasets.

The three dimensional (3D) reconstruction of urban areas using Interferometric Synthetic Aperture Radar (InSAR) systems can be particularly difficult to be carried out due to presence of high interferometric phase jumps. These discontinuities, generated from the presence of man-made structures, make the unwrapping problem, needed for the 3D reconstruction non trivial. In this paper we propose an approach for phase unwrapping, and consequently for 3D reconstruction, that exploits multiple acquired interferograms and combines them using the Extended Kalman Filter (EKF). Differently form other EKF based approach phase unwrapping algorithms, the proposed technique is able to take into account and correctly solving height discontinuities, making the algorithm particularly suitable for 3D reconstruction in urban areas. The methodology has been tested on real high resolution datasets, showing interesting results.

Brain tissue segmentation in Magnetic Resonance Imaging is useful for a wide range of applications. Classical approaches exploit the gray levels image and implement criteria for differentiating regions. Within this paper a novel approach for brain tissue joint segmentation and classification is presented. Starting from the estimation of proton density and relaxation times, we propose a novel method for identifying the optimal decision regions. The approach exploits the statistical distribution of the involved signals in the complex domain. The technique, compared to classical threshold based ones, is able to globally improve the classification rate. The effectiveness of the approach is evaluated on both simulated and real datasets.

Brain tissue classification in Magnetic Resonance Imaging is useful for a wide range of applications. Within this manuscript a novel approach for brain tissue joint segmentation and classification is presented. Starting from the relaxation time estimation, we propose a novel method for identifying the optimal decision regions. The approach exploits the statistical distribution of the involved signals in the complex domain. The technique, compared to classical threshold based ones, is able to improve the correct classification rate. The effectiveness of the approach is evaluated on a simulated case study.

In recent years a growing interest has grown in Magnetic Resonance images segmentation techniques, due to their usefulness in many applications. Within this manuscript, a novel segmentation approach is presented, based on two main innovations. First, it exploits the estimated proton density and relaxation times for each pixel, instead of its gray-level intensity. This feature makes the algorithm particularly robust and allows the classification of identified segments. Secondly, it implements a specifically evolved version of the DBSCAN approach, gaining advantages in the effectiveness of region estimation. The technique, compared to an euclidean distance based one, is able to improve the correct classification rate. The effectiveness of the approach is evaluated on a simulated case study, and will be extended to real data within next weeks.

A novel Phase Unwrapping (PhU) technique for InSAR interferometric stacks based on statistical estimation theory is presented. The approach is intended for exploiting both amplitude and phase of the acquired data in order to express the multi-baseline likelihood function without the assumption of independence among channels, i.e. by considering the full mutual correlation matrix. Moreover, the contextual information is adopted for regularizing the solution, obtaining a Maximum A Posteriori (MAP) estimator. First results on real datasets related to an urban scenario are presented, showing the interesting performances of the proposed method in terms of Digital Elevation Model (DEM) reconstruction.

Within this manuscript a novel technique for joint Digital Elevation Model (DEM) reconstruction and deformation estimation is presented. In particular, a Maximum A Posteriori (MAP) estimator that makes use of Gaussian Markov Random Fields (MRF) is proposed. The advantage of the approach, with respect to classical Permanent Scatterers (PS) based techniques, consists of its ability to evaluate the height and the deformation for all resolution cell across the scene, instead of only strong scatterers. Thus, the method is able to work also in natural scenarios, or in general when few PS are available. First results are presented on a simulated dataset with COSMO-SkyMed acquisition parameters.

Image formation in Magnetic Resonance Imaging (MRI) is the procedure which allows the generation of the image starting from data acquired in the so called k-space. At the present, many image formation techniques have been presented, working with different k-space filling strategies. Recently, Compressive Sampling (CS) has been successfully used for image formation from non fully sampled k-space acquisitions, due to its interesting property of reconstructing signal from highly undetermined linear systems. The main advantage consists in greatly reducing the acquisition time. Within this manuscript, a novel application of CS to MRI field is presented, named Intra Voxel Analysis (IVA). The idea is to achieve the so-called super resolution, i.e. the possibility of distinguish anatomical structures smaller than the spatial resolution of the image. For this aim, multiple Spin Echo images acquired with different Echo Times are required. The output of the algorithm is the estimation of the number of contributions present in the same pixel, i.e. the number of tissues inside the same voxel, and their spin-spin relaxation times. This allows us not only to identify the number of involved tissues, but also to discriminate them. At the present, simulated case studies have been considered, obtaining interesting and promising results. In particular, a study on the required number of images, on the estimation noise and on the regularization parameter of different CS algorithms has been conducted. As future work, the method will be applied to real clinical datasets, in order to validate the estimations.

Many pathologies can be identified by evaluating differences raised in the physical parameters of involved tissues. In a Magnetic Resonance Imaging (MRI) framework, spin-lattice T1 and spin-spin T2 relaxation time parameters play a major role in such an identification. In this manuscript, a theoretical study related to the evaluation of the achievable performances in the estimation of relaxation times in MRI is proposed. After a discussion about the considered acquisition model, an analysis on the ideal imaging acquisition parameters in the case of spin echo sequences, i.e., echo and repetition times, is conducted. In particular, the aim of the manuscript consists in providing an empirical rule for optimal imaging parameter identification with respect to the tissues under investigation. Theoretical results are validated on different datasets in order to show the effectiveness of the presented study and of the proposed methodology.

Multibaseline interferometric synthetic aperture radar (In-SAR) systems are used to obtain the height profile of the observed ground scene with high accuracy. The techniques that are commonly used exploit only the interferometric phase information, and are based on maximum likelihood (ML) estimation. Due to the difficulty of expressing the multi-baseline likelihood function in closed form, they adopt the statistical independence approximation of the interferometric phases. In this paper, we investigate the effect of this approximation, and analyze the performance of two statistical methods exploiting complex SAR images, which takes into account the mutual correlation among all the interferometric images. These two methods are based on a local and a contextual approach. The local one is based on an ML estimator, and processes each pixel independently from the neighboring ones. The contextual approach adopts a Bayesian estimator in order to regularize and improve the height reconstruction. The presented results show that the inclusion of SAR image correlation information allows to improve the height reconstruction accuracy. The proposed method also provides an estimation of speckle-reduced image intensity.